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145 | class AI21(CustomLLM):
"""AI21 Labs LLM.
Examples:
`pip install llama-index-llms-ai21`
```python
from llama_index.llms.ai21 import AI21
llm = AI21(model="j2-mid", api_key=api_key)
resp = llm.complete("Paul Graham is ")
print(resp)
```
"""
model: str = Field(description="The AI21 model to use.")
maxTokens: int = Field(description="The maximum number of tokens to generate.")
temperature: float = Field(description="The temperature to use for sampling.")
additional_kwargs: Dict[str, Any] = Field(
default_factory=dict, description="Additional kwargs for the anthropic API."
)
_api_key = PrivateAttr()
def __init__(
self,
api_key: Optional[str] = None,
model: Optional[str] = "j2-mid",
maxTokens: Optional[int] = 512,
temperature: Optional[float] = 0.1,
additional_kwargs: Optional[Dict[str, Any]] = None,
callback_manager: Optional[CallbackManager] = None,
system_prompt: Optional[str] = None,
messages_to_prompt: Optional[Callable[[Sequence[ChatMessage]], str]] = None,
completion_to_prompt: Optional[Callable[[str], str]] = None,
pydantic_program_mode: PydanticProgramMode = PydanticProgramMode.DEFAULT,
output_parser: Optional[BaseOutputParser] = None,
) -> None:
"""Initialize params."""
additional_kwargs = additional_kwargs or {}
callback_manager = callback_manager or CallbackManager([])
api_key = get_from_param_or_env("api_key", api_key, "AI21_API_KEY")
self._api_key = api_key
super().__init__(
model=model,
maxTokens=maxTokens,
temperature=temperature,
additional_kwargs=additional_kwargs,
callback_manager=callback_manager,
system_prompt=system_prompt,
messages_to_prompt=messages_to_prompt,
completion_to_prompt=completion_to_prompt,
pydantic_program_mode=pydantic_program_mode,
output_parser=output_parser,
)
@classmethod
def class_name(self) -> str:
"""Get Class Name."""
return "AI21_LLM"
@property
def metadata(self) -> LLMMetadata:
return LLMMetadata(
context_window=ai21_model_to_context_size(self.model),
num_output=self.maxTokens,
model_name=self.model,
)
@property
def _model_kwargs(self) -> Dict[str, Any]:
base_kwargs = {
"model": self.model,
"maxTokens": self.maxTokens,
"temperature": self.temperature,
}
return {**base_kwargs, **self.additional_kwargs}
def _get_all_kwargs(self, **kwargs: Any) -> Dict[str, Any]:
return {
**self._model_kwargs,
**kwargs,
}
@llm_completion_callback()
def complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
all_kwargs = self._get_all_kwargs(**kwargs)
ai21.api_key = self._api_key
response = ai21.Completion.execute(**all_kwargs, prompt=prompt)
return CompletionResponse(
text=response["completions"][0]["data"]["text"], raw=response.__dict__
)
@llm_completion_callback()
def stream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseGen:
raise NotImplementedError(
"AI21 does not currently support streaming completion."
)
@llm_chat_callback()
def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
all_kwargs = self._get_all_kwargs(**kwargs)
chat_fn = completion_to_chat_decorator(self.complete)
return chat_fn(messages, **all_kwargs)
@llm_chat_callback()
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
raise NotImplementedError("AI21 does not Currently Support Streaming Chat.")
|